How to become a Data Scientist

Every organization's Data scientists are the game changer. They are doing the task of collecting, analyzing, and interpreting data from enormous volumes (known as "Big data") of data. They make sense of structured and unstructured data by interpreting them and coming up with the best outcomes. With that, they assist organizations in making crucial decisions for their business and growth. Utilizing cutting–edge analytics tools, such as Machine Learning and predictive modeling, is essential for this role.

What is Data Science?

Want to become a data scientist? First, know what data science is. Data science is one of the thriving fields of the 21st century. Data science is not a big deal. According to Professor David Attenborough of the University of British, "It simply combines Statistics, Machine Learning, and one spoonful of Data analysis with decision-making abilities" Combining data analysis, analysis, and decision-making abilities is the art of data science. You are already a data scientist if you have those skills.

Data scientist role:

You might wonder what data scientist does. What is their role in the business world? And why are they important?

Here are some rules and responsibilities of a data scientist:

• The extraction of insights from valuable data sources is known as data mining.
• Utilizing machine learning technologies to choose features, build, and improve classifiers.
• Performing structured and unstructured data preparation.
• Improving data capture techniques to obtain all pertinent data for creating analytical systems.
• Processing, cleansing, and verifying the accuracy of data for analysis.
• Evaluating a bunch of data to hunt for trends and solutions.
• Creating prediction systems and machine learning algorithms
• Offer tactics and solutions to address corporate difficulties.
• Work together with the business and IT teams.

·       Is it worth a shot?

Data science is becoming one of the most promising and demanding fields. It's not going down anytime soon - at least another 50 years. The demand for data scientists is high but the supply is short. That's giving you a better opportunity to get hired by major organizations. And it's a high-yielding field. Choosing a profession as a data scientist is worth a shot for a promising future.

How to become a data scientist?

Compared to 15 years ago, a massive number of data is produced in daily life. To extract insights from those big data you need to have strong mathematical and computer skills as well as experience. Here are some steps to consider.

1. Develop the right data skill

To become a data scientist you don't have to have a degree in data science. But you need to have the required skills. You don't have to learn everything but learning essential skills is important. Develop your skills sets in:

Programming language

Data scientists mostly rely on the programming language to manage large chunks of data. Some important languages are

• Python
• R
• Query languages like SQL and NoSQL
• RStudio Server
• Hive
• SAS

·     Statistics

You need to have basic knowledge and understanding skills in Linear Algebra, Correlational Calculus, Coordinate Geometry, ANOVA And Probability, Matrix, and Regression.

Machine Learning

Including Machine Learning and deep learning in your job will allow you to continuously improve the quality of the information you collect and perhaps even forecast the outcomes of future datasets.

Data cleaning

By ensuring that you only have the most recent records and [ertinent files, data cleansing makes it simple to find them whenever you need them. 

Data visualization

Present your findings to the audience using data visualization which includes graphs and charts

The best way to way to present your data is through charts and graphs. That is where data visualization plays its role. Tableau, PowerBI, Excel, Ploty, and Infogram are some excellent tools that are available in the market.

2. Become a master

Practice makes you better. When you learn essential tools, statistics, machine learning, and algorithms practice them by doing data science projects. Sharpens your skills by doing different types of projects. Try to learn a new thing every day. Stay trend and effective. It helps you to build and acquire strong skills.

Understand the companies/ market/ business needs. Start from scratch. Collect the right data then clean the dirty data and test it. Apply some algorithms, statistics, and analytics tools to get the required data and create modeling data. With those turn your data into readable and processable one. And present them to the employer. 

3. Let the data speak for itself

The main stage is presenting your data. Your presentation shows your skills, capability, and your worth. So let the data speak for itself and for you too. Accompany the data with your compelling narrative and demonstrate the problem and solution to tackle the problems to your employer.

4. Hunt down the job

After acquiring the necessary abilities, look for an entry-level position in the field. Look for jobs that utilize data. You can fill out an application for a position as a statistician, data engineer, business intelligence analyst, or data analyst. When you grow professionally and broaden your expertise, you eventually create together into a data scientist.

In conclusion

It's a myth anyone can become a scientist. However, a person who has a love for data, a passion for computing, and superb analytical abilities can become a data scientist. To become a data scientist, you must invest time and effort.